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マルチエージェント経路計画arXiv:2312.06314

DMS*: マルチエージェント組合せ経路計画におけるメイクスパン最小化

DMS*: Minimizing Makespan for Multi-Agent Combinatorial Path Finding

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複数エージェントが目標地点を訪問しつつ衝突回避経路を計画する問題で、最大到着時間を最小化する手法を提案し、計算を効率化する遅延戦略を導入した。

著者: Zhongqiang Ren, Anushtup Nandy, Sivakumar Rathinam, Howie Choset

分類: cs.RO, cs.AI

原文アブストラクト

Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for multiple agents from their initial to goal locations, while visiting a set of intermediate target locations in the middle of the paths. MCPF is challenging as it involves both planning collision-free paths for multiple agents and target sequencing, i.e., solving traveling salesman problems to assign targets to and find the visiting order for the agents. Recent work develops methods to address MCPF while minimizing the sum of individual arrival times at goals. Such a problem formulation may result in paths with different arrival times and lead to a long makespan, the maximum arrival time, among the agents. This paper proposes a min-max variant of MCPF, denoted as MCPF-max, that minimizes the makespan of the agents. While the existing methods (such as MS*) for MCPF can be adapted to solve MCPF-max, we further develop two new techniques based on MS* to defer the expensive target sequencing during planning to expedite the overall computation. We analyze the properties of the resulting algorithm Deferred MS* (DMS*), and test DMS* with up to 20 agents and 80 targets. We demonstrate the use of DMS* on differential-drive robots.

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